@InProceedings{Chaparro-CruzMont:2021:BoSuDe,
               author = "Chaparro-Cruz, Israel N. and Montoya-Zegarra, Javier A.",
          affiliation = "Department of Computer Science, Universidad Cat{\'o}lica San 
                         Pablo, Arequipa, Per{\'u}  and Institute for Biomedical 
                         Engineering, ETH Zurich, Zurich, Switzerland",
                title = "BORDE: Boundary and Sub-Region Denormalization for Semantic Brain 
                         Image Synthesis",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "brain imaging, generative adversarial networks, normalization 
                         layers, semantic image synthesis.",
             abstract = "Medical images are often expensive to acquire and offer limited 
                         use due to legal issues besides the lack of consistency and 
                         availability of image annotations. Thus, the use of medical 
                         datasets can be restrictive for training deep learning models. The 
                         generation of synthetic images along with their corresponding 
                         annotations can therefore aid to solve this issue. In this paper, 
                         we propose a novel Generative Adversarial Network (GAN) generator 
                         for multimodal semantic image synthesis of brain images based on a 
                         novel denormalization block named BOundary and sub-Region 
                         DEnormalization (BORDE). The new architecture consists of a 
                         decoder generator that allows: (i) an effectively sequential 
                         propagation of a-priori semantic information through the 
                         generator, (ii) noise injection at different scales to avoid 
                         mode-collapse, and (iii) the generation of rich and diverse 
                         multimodal synthetic samples along with their contours. Our model 
                         generates very realistic and plausible synthetic images that when 
                         combined with real data helps to improve the accuracy in brain 
                         segmentation tasks. Quantitative and qualitative results on 
                         challenging multimodal brain imaging datasets (BraTS 2020 and 
                         ISLES 2018) demonstrate the advantages of our model over existing 
                         image-agnostic state-of-the-art techniques, improving segmentation 
                         and semantic image synthesis tasks. This allows us to prove the 
                         need for more domain-specific techniques in GANs models.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00020",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00020",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45D397B",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45D397B",
           targetfile = "70.pdf",
        urlaccessdate = "2024, May 06"
}